White Papers

Accelerating Big Data – Using SanDisk SSDs for Apache Hbase Workloads

This white paper shows the benefits of using SanDisk SSDs in conjunction with an Apache HBase deployment, using the “Yahoo! Cloud Serving Benchmark” (YCSB) – with update-heavy and read-heavy workloads.

Executive Summary

In today’s hyper-connected world, there is a significant amount of data being collected, and later analyzed, to make business decisions. This explosion of data has led to various analytics technologies that can operate on this “Big Data”, which stretches into the petabyte (PB) range within organizations, and into the zettabyte (ZB) range worldwide.

Traditional database systems and data warehouses are being augmented with newer scale-out technologies like Apache Hadoop and with NoSQL databases like Apache HBase, Cassandra and MongoDB to manage the massive scale of data being processed today, and analyzed by the organizations that use these databases.

This technical paper describes the benchmark testing conducted by SanDisk, using SanDisk SSDs in conjunction with an Apache HBase deployment. The testing uses the “Yahoo! Cloud Serving Benchmark” (YCSB), and 64GB and 256GB data sets running on a single-node Apache HBase deployment. This testing had a focus on update-heavy and read-heavy YCSB workloads. The primary goal of this paper is to show the benefits of using SanDisk solid-state drives (SSDs) within an HBase environment.

SanDisk SSDs help boost the performance in HBase environments by providing 30x to 40x more transactions/sec (TPS), when compared to HDD configurations, and very low latencies of less than 50 seconds compared to > 1,000 seconds with HDDs.

These performance improvements result in significantly improved efficiency of data-analytics operations by providing faster time to results, and thus providing cost savings related to reduced operational expenses for business organizations.

Apache HBase

Apache HBase is an open-source, distributed, scalable, big-data database. It is a non-relational database (also called a NoSQL database), unlike many of the traditional relational database management systems (RDBMS). Apache HBase uses the Hadoop Distributed File System (HDFS) in distributed mode.

HBase can also be deployed in standalone mode, and in this case it uses the local file system. It is used for random, real-time read/write access to large quantities of sparse data. Apache HBase uses Log Structured Merge trees (LSM trees) to store and query the data. It features compression, in-memory caching, and very fast scans. Importantly, HBase tables can serve as both the input and output for MapReduce jobs.

Some major features of HBase that have proven effective in supporting analytics workloads are:

Linear and modular scalability

Strictly consistent reads and writes

Automatic and configurable sharding of tables, with shards being smaller and more manageable parts of large database tables

Support for fault tolerant and highly available processing, through using an automatic failover support between RegionServers

Uses the Java API for client access, as well as Thrift or REST gateway APIs, thus providing applications with a variety of access methods to the data

Near-real-time query support

Support for exporting metrics via the Hadoop metrics subsystem to files or the Ganglia open source cluster management and administration tool

The HBase architecture defines two different storage layers, the MemStore and the StoreFile. An object is written into MemStore first, and when the MemStore is filled to capacity, a flush-to-disk operation is requested. This flush-to-disk operation results in the MemStore contents being written to disk. HBase performs compaction/compression during the flush-to-disk operations (which are more frequent for heavy write workloads), merging different StoreFiles together.

HBase read requests will try to read an object first from the MemStore. In case of a read miss, the read operation is fulfilled from the StoreFile. With a heavy read workload, the number of concurrent read operations from the disk increases accordingly.

The HBase storage architecture for a distributed HBase instance is shown in Figure 1. The figure shows the various components within HBase, including the HBase Master and the HBase Region Servers and it also shows how the various components access the underlying files residing in HDFS. Please visit some of the references in the References section for detailed information about the HBase Architecture.

Figure 1:HBase Components and HDFS

YCSB Benchmark

The Yahoo! Cloud Serving Benchmark (YCSB) consists of two components:

The client, which generates the load according to a workload type and records the latency and throughput associated with that workload.

The workload files, which define the workload type by describing the size of the data set, the total number of requests, the ratio of read and write queries.

There are six major workload types in YCSB:

Update-Heavy Workload A: 50/50 update/read ratio

Read-Heavy Workload B: 5/95 update/read ratio

Read-Only Workload C: 100% read-only, maps closely to Workload B.

Read-Latest Workload D: 5/95 insert/read ratio, the read load is skewed towards the end of the key range, has similarities to Workload B.

Short-Ranges Workload E: 5/95 insert/read ratio, over a range of up to 100 records

Read/modify/write Workload F: 50/50 write/read ratio, similar to Workload A, but the writes are actual updates/modifications rather than just blind writes like in Workload A.

For additional information about YCSB and workload types, please visit the official YCSB page, as listed in the References section of this paper.

This technical paper describes the first three types of Workloads, A, B and C, and the latency and transactions-per-second results for these workloads using different storage configurations. These workloads provide a good mix of update-heavy and read-heavy test scenarios.

Test Design

A standalone HBase instance using the local file system was set up for the purpose of determining the benefits of using solid-state disks within an HBase environment. The testing was conducted with the YCSB benchmark, using different workloads (Workloads A, B and C) and different workload dataset sizes (64GB and 256GB). The testing also included HBase, as it was deployed on different storage configurations. The transactions per second (TPS) and latency results for each of these tests were collected and analyzed. The results are summarized in the Test Validation and Results Summary section of this paper.

Test Environment

The test environment consisted of one Dell PowerEdge™ R720 server with two Intel® Xeon® CPUs (processors), with 12 cores per CPU) and 96GB of DRAM, which served as the HBase server, and one Dell PowerEdge R720, which served as a client to the test HBase server (a client to the test HBase server) running the YCSB workloads. For testing purposes, a 1GbE network interconnect was used between the server and the client. The local storage configuration on the HBase server varied between an all-HDD and an all-SSD configuration (more details are provided in the Test Methodology section of this paper). The dataset sizes used by the YCSB workloads on the clients were 64GB and 256GB.

The Apache HBase server is a Dell PowerEdge R720 with two Intel Xeon 2.00GHz CPU E5-2620 and 96GB of memory. The client's compute infrastructure is the same as the server.

Network Infrastructure

The client and the HBase server are connected to a 1GbE network via the onboard 1GbE NICs using a Dell PowerConnect 2824 24-port switch. This network was used as the primary means of communication between the client server and the HBase server.

Storage Infrastructure

The Apache HBase server uses six 500GB 7.2K RPM Dell SATA HDDs in a RAID0 configuration for the all-HDD configuration. The HDDs are replaced by six 480GB CloudSpeed Ascend SATA SSDs for the all-SSD test configuration.

The RAID0 volume is formatted with the XFS file system and mounted for use within HBase.

HBase configuration

Most of the default HBase configuration parameters were used for testing purposes across all of the workloads and storage configurations under test. A few HBase configuration parameters were modified during the testing, and these are listed in the tables, below:

Name of parameter

Default

Modified

hbase.hregion.memstore.chunkpool.maxsize

0

0.5

hbase.hregion.max.filesize

1073741824

53687091200

hbase.regionserver.handler.count

10

150

Table 3:HBase configuration parameter for hbase-env.sh

Name of parameter

Default

Modified

hbase.hregion.memstore.chunkpool.maxsize

0

0.5

hbase.hregion.max.filesize

1073741824

53687091200

hbase.regionserver.handler.count

10

150

hbase.block.cache.size

0.2

0.4

hbase.hregion.memstore.mslab.enabled

False

True

habse.hregion.memstore.mslab.maxallocation

262144

262144

Table 4:HBase configuration parameter for hbase-site.xml

Test Validation and Results Summary

Test Methodology

The primary goal of this technical paper is to showcase the benefits of using SSDs within an HBase environment. To achieve this goal, SanDisk tested three main YCSB workloads (Workloads A, B and C) against multiple tests configurations. The distinct test configurations were set up by varying the underlying storage configuration for the standalone HBase server (using either an all-HDD or an all-SSD configuration), and the size of the data set (both 64GB and 256GB datasets were used in the testing). These storage configuration and data size variations are described below:

Storage Configuration

Storage configuration for the standalone HBase instance was varied between an all-HDD and an all-SSD configuration:

1. All-HDD configuration: The HBase server uses a single RAID0 volume with 6 HDDs for the single instance HBase.

2. All-SSD configuration: In this configuration, the HDDs of the first configuration are replaced with SSDs and a corresponding RAID0 volume for use with the HBase instance.

YCSB Workload Dataset Size

Two YCSB dataset sizes were used:

1. 64GB dataset size: This particular dataset is smaller than the size of the available memory. As a result, all of the data is stored in-memory, thus avoiding disk I/O as much as possible.

2. 256GB dataset size: This dataset size is larger than the size of the available memory, therefore requiring more disk I/O operations as compared to the smaller 64GB dataset size.

Update-Heavy Workload (Workload A)

The YCSB Workload A is referred to as an update-heavy workload. It is a workload with a 50/50 ratio of updates and reads. The distribution of the update and read operations can be uniformly spread out across the entire data set–“uniform” distribution–or can be of type named “Zipfian”, in which case a portion of the dataset is accessed more frequently than the rest of the dataset. A Zipfian distribution is named after Zipf's Law, an empirical law of mathematical statistics, regarding statistical distribution of data.

Workload A models the behavior of applications like a session store, where recent user actions are recorded.

Throughput TPS Results
The throughput results for YCSB Workload A are summarized in Figure 2. The X axis on the graph shows the different dataset sizes (64GB and 256GB). The Y axis shows the throughput in terms of transactions per second. For each dataset size, the results for the uniform and Zipfian distributions are shown for the all-HDD and all-SSD configuration via different colored bars (see legend in figures for more details). These same results are summarized in Table 5.

YCSB Workload Types

Dataset Size

YCSB Workload Distribution

TPS All-HDD

TPS All-SSD

Workload A (50r/50w)

64GB

Uniform

3,263

21,681

Workload A (50r/50w)

64GB

Zipfian

5,705

27,561

Workload A (50r/50w)

256GB

Uniform

411

13,925

Workload A (50r/50w)

256GB

Zipfian

748

23,769

Table 5: Throughput results summary for Workload A

Latency Results
The read latency results for YCSB Workload A are summarized in Figure 3. The X axis on the graph shows the read latencies for the different dataset sizes (64GB and 256GB), for both uniform and Zipfian distributions, and the Y axis shows the latency in seconds. These same results are summarized in Table 6.

The write latency results for YCSB Workload A are not shown in the figures. HBase uses a write-ahead log, which writes any updates/edits to memory, and these updates are later flushed to disk when the flush interval is reached. Due to the use of memory for updates/edits, the write latencies are between 0-1 milliseconds (ms), which get recorded as 0 seconds. As a result, the write latencies are not shown in the figures. They are, however, recorded in Table 6.

Figure 3: Latency comparisons for Workload A

YCSB Workload Types

Storage Configuration

YCSB Workload Distribution

64GB

256GB

Read

Write

Read

Write

Workload A (50r/50w)

HDD

Uniform

505

0

>1,000

0

Workload A (50r/50w)

SSD

Uniform

36

0

38

0

Workload A (50r/50w)

HDD

Zipfian

278

9

>1,000

0

Workload A (50r/50w)

SSD

Zipfian

33

0

30

0

Table 6: Latency results summary for Workload A

Read-Intensive Workload (Workload B)

YCSB Workloads B and C are read-intensive workloads. Workload B generates 95% read operations, with just 5% update operations in that workload. Workload C is a 100% read workload. Again, for both these workloads, the frequency of access across all parts of the dataset can be uniform, or a Zipfian distribution of access can be used where part of the data is being accessed more frequently.

These workloads simulate applications like photo-tagging, where most operations involve reading tags–or user profile caches, and 100% of the workload is reading from the user profile caches. These types of workloads are being increasingly adopted today, especially for Web-enabled cloud-computing workloads and social media workloads.

Throughput Results
The throughput results for YCSB Workload B are summarized in Figure 4. The X axis on the graph shows the different dataset sizes (64GB and 256GB). The Y axis shows the throughput in terms of transactions per second. For each dataset size, the results for the uniform and Zipfian distributions are shown for the all-HDD and all-SSD configuration via different colored bars. These same results are summarized in Table 7.

Figure 4: Throughput comparison for Workload B

YCSB Workload Types

Dataset Size

YCSB Workload Distribution

TPS All-HDD

TPS All-SSD

Workload B (95r/5w)

64GB

Uniform

2,186

16,051

Workload B (95r/5w)

64GB

Zipfian

3,870

20,169

Workload B (95r/5w)

256GB

Uniform

219

8,193

Workload B (95r/5w)

256GB

Zipfian

410

14,943

Table 7: Throughput results summary for Workload B

Latency results
The read latency results for YCSB Workload B are summarized in Figure 5. The X axis on the graph shows the read latencies for the different dataset sizes (64GB and 256GB), for both uniform and Zipfian distributions, and the Y axis shows the latency in seconds. These same results are summarized in Table 8.

The write latency results for YCSB Workload B are not shown in the figures. Since this particular workload is a read-heavy workload, there is a very small proportion of writes for Workload B. As a result, write latencies are not significant for this workload. For the small proportion of writes, HBase writes any updates/edits to memory, with latencies of 0-1 milliseconds (ms) which get recorded by YCSB as 0 seconds.

Figure 5: Latency comparisons for Workload B

YCSB Workload Types

Storage Configuration

YCSB Workload Distribution

64GB

256GB

Read

Write

Read

Write

Workload B (50r/50w)

HDD

Uniform

427

0

>1,000

0

Workload B (50r/50w)

SSD

Uniform

29

0

37

0

Workload B (50r/50w)

HDD

Zipfian

76

0

>1,000

0

Workload B (50r/50w)

SSD

Zipfian

23

0

31

0

Table 8: Latency results summary for Workload B

Read-Only Workload (Workload C)

Throughput Results
The throughput results for YCSB Workload C are summarized in Figure 6. The X axis on the graph shows the different dataset sizes (64GB and 256GB). The Y axis shows the throughput in terms of transactions per second. For each dataset size, the results for the uniform and Zipfian distributions are shown for the all-HDD and all-SSD configuration via different colored bars (see the legend in the figures for more details). These same results are summarized in Table 9.

Figure 6: Throughput comparison for Workload C

YCSB Workload Types

Dataset Size

YCSB Workload Distribution

TPS All-HDD

TPS All-SSD

Workload C (100r/0w)

64GB

Uniform

1,865

15,334

Workload C (100r/0w)

64GB

Zipfian

3,582

19,756

Workload C (100r/0w)

256GB

Uniform

209

7,799

Workload C (100r/0w)

256GB

Zipfian

373

14,214

Table 9: Throughput results summary for Workload C

Latency Results
The read latency results for YCSB Workload C are summarized in Figure 7. The X axis on the graph shows the read latencies for the different dataset sizes (64GB and 256GB), for both uniform and Zipfian distributions, and the Y axis shows the latency in seconds. These same results are summarized in Table 10.

The write latency results for YCSB Workload C are not shown in the figures. Since this particular workload is a read-only workload, there are no write operations in Workload C. As a result, write latencies are not applicable for this workload.

Figure 7: Latency comparisons for Workload C

YCSB Workload Types

Storage Configuration

YCSB Workload Distribution

64GB

256GB

Read

Write

Read

Write

Workload C (50r/50w)

HDD

Uniform

463

0

>1,000

0

Workload C (50r/50w)

SSD

Uniform

28

0

37

0

Workload C (50r/50w)

HDD

Zipfian

124

0

>1,000

0

Workload C (50r/50w)

SSD

Zipfian

22

0

31

0

Table 10: Latency results summary for Workload C

Conclusion

The key takeaways from the YCSB tests for Apache HBase, comparing all-HDD and all-SSD storage configurations, are as follows:

SanDisk SSDs show a dramatic increase (30-40x) in number of transactions per second for update- heavy and read-heavy workloads for different types of data access, whether uniform across the dataset or targeted to only a portion of the dataset. This translates to a higher rate of query processing in a variety of different data analytics applications,

Summary

The higher transactions/second and lower latency results seen with CloudSpeed® SSDs directly translate to faster query processing, thus reducing the time-to-results. In an organization's IT department, this results in significant improvements in business process efficiency and therefore cost-savings.

The tests described in this paper clearly demonstrate the immense benefits of deploying SanDisk's CloudSpeed SATA solid-state drives (SSDs) drives within HBase environments. Using CloudSpeed drives will help deploy a cost-effective and process-efficient data analytics environment by providing increased throughput and reduced latency for data analytics queries.

It is important to understand that workloads will vary significantly in terms of their I/O access patterns. Therefore, all workloads many not benefit equally from the use of SSDs for an HBase deployment. For the most effective deployments, customers will need to develop a proof of concept for their deployment and workloads, so that they can evaluate the benefits that SSDs can provide to their organization.